PySpark, setting spark conf values in a function and catching for errors

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PySpark, setting spark conf values in a function and catching for errors

Mich Talebzadeh

Hi,


I have multiple routines that are using Spark for Google BigQuery that set these configuration values. I have decided to put them in a PySpark function as below with spark as an input.


def setSparkConfSet(spark):

    try:

        spark.conf.set("GcpJsonKeyFile", config['GCPVariables']['jsonKeyFile'])

        spark.conf.set("BigQueryProjectId", config['GCPVariables']['projectId'])

        spark.conf.set("BigQueryDatasetLocation", config['GCPVariables']['datasetLocation'])

        spark.conf.set("google.cloud.auth.service.account.enable", "true")

        spark.conf.set("fs.gs.project.id", config['GCPVariables']['projectId'])

        spark.conf.set("fs.gs.impl", "com.google.cloud.hadoop.fs.gcs.GoogleHadoopFileSystem")

        spark.conf.set("fs.AbstractFileSystem.gs.impl", "com.google.cloud.hadoop.fs.gcs.GoogleHadoopFS")

        spark.conf.set("temporaryGcsBucket", config['GCPVariables']['tmp_bucket'])

    except:

        print(f"""Could not set spark config variables, quitting""")

        sys.exit(1)


Two questions


Is it necessary to catch any error or simply call the function in the main routine or do the error handling in the module that is calling this function?


Thanks



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